Official repository for MOSS-Image, a 5B-parameter base DiT model with high-quality text rendering, layout generation, and efficient fine-tuning.
🧠 Train a 64M-parameter LLM from scratch in just 2h!
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.
A 2.78-trillion-parameter Kimi K3 running inference on a single CPU in 8.24 GB of RAM. Portable C99: no BLAS, no framework, no GPU.
An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.
Open-source GPU cluster orchestrator and PyTorch-based framework for training LLMs with billions to trillions of parameters.
A 14M-parameter LLM keeps a virtual fish tank alive on an $8 ESP32-S3 — distilled from a 26B teacher into 7.56 MB, no network.
DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
Elegant HTTP Networking in Swift
Use PEFT or Full-parameter to CPT/SFT/DPO/GRPO 600+ LLMs (Qwen3.8, DeepSeek-V4, GLM-5.1, InternLM3, Llama4, ...) and 300+ MLLMs (Qwen3-VL, Qwen3-Omni, InternVL3.5, Ovis2.5, GLM5.3, Gemma4, Llava, Phi4, ...) (AAAI 2025).
Fast and Accurate ML in 3 Lines of Code
👀 Train a 65M-parameter VLM from scratch in just 2h!
CasADi is a symbolic framework for numeric optimization implementing automatic differentiation in forward and reverse modes on sparse matrix-valued computational graphs. It supports self-contained C-code generation and interfaces state-of-the-art codes such as SUNDIALS, IPOPT etc. It can be used from C++, Python, Matlab/Octave, Julia or Javascript
Tiny, fast and accurate Turkish text-to-speech. 8.6M parameters, 0.92% WER on Freya-TR-Eval, first audio in ~4 ms and 1,300× real time on one GPU. Streams audio, batches many callers on one GPU, and runs offline on a GPU or CPU.
Gradient descent, but the parameters are agents — a parallel, asynchronous framework for self-evolving agents (skills, prompts, harnesses). Diffs are the gradients; the aggregator is the optimizer.
A visual no-code/code-free web crawler/spider易采集:一个可视化浏览器自动化测试/数据采集/网页爬虫软件,可以无代码图形化的设计和执行爬虫任务。别名:ServiceWrapper面向Web应用的智能化服务封装系统。
The AI Compute Platform for frontier teams. SkyPilot turns fragmented AI compute into one AI supercomputer, so frontier AI teams build custom intelligence faster.
A Flexible and Powerful Parameter Server for large-scale machine learning
Train the smallest LM you can that fits in 16MB. Best model wins!
A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.
A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.
Digital Audio Workstation with Python; VST instruments/effects, parameter automation, FAUST, JAX, Warp Markers, and JUCE processors
Naive-N0.5-Flash is an open-weight 309B MoE model with 15.5B active parameters, built for coding and AI R&D.
A 45M-parameter LLM: needle 2 running fully offline on an ESP32-S3, turning plain-English requests into guaranteed-valid JSON tool calls.
A SwiftUI-lean GPUI component library, style flows through the environment, never through parameters.
An 8M-parameter English text-to-speech model distilled from Kokoro-82M. 9 MB, runs 18x real time on one CPU thread.
BarunLM-35M: a parameter-efficient 35M base language model trained on 5.7B tokens
A hyperparameter optimization framework
The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.
Type-safe search params state manager for React frameworks - Like useState, but stored in the URL query string.
Distributed Asynchronous Hyperparameter Optimization in Python
Find the local LLM that actually runs and performs best on your hardware. Ranked by real, recency-aware benchmarks, not parameter count. One command, run it instantly.
📚 Parameterize, execute, and analyze notebooks
AI Infra / AI Orchestration / AI Control Plane
Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
Run the full 2.78-trillion-parameter Kimi K3 model, DeepSeek V4.1 Flash or GLM-5.3-Flash beyond available RAM by streaming activated weights directly from NVMe. A dependency-free, embeddable C inference engine.
Repository hosting code for "Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations" (https://arxiv.org/abs/2402.17152).
A Julia machine learning framework
Automated Machine Learning on Kubernetes
Machine Learning in R
Python-based research interface for blackbox and hyperparameter optimization, based on the internal Google Vizier Service.
Hyperparameter Experiments with TensorFlow and Keras
Custom CSS theme for doxygen html-documentation with lots of customization parameters.
GemPy is an open-source, Python-based 3-D structural geological modeling software, which allows the implicit (i.e. automatic) creation of complex geological models from interface and orientation data. It also offers support for stochastic modeling to address parameter and model uncertainties.
bUnit is a testing library for Blazor components that make tests look, feel, and runs like regular unit tests. bUnit makes it easy to render and control a component under test’s life-cycle, pass parameter and inject services into it, trigger event handlers, and verify the rendered markup from the component using a built-in semantic HTML comparer.
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
A library to help you dial in interface parameters of any kind
Non-Linear Least Squares Minimization, with flexible Parameter settings, based on scipy.optimize, and with many additional classes and methods for curve fitting.
C++ libraries and programs demonstrating mesh processing research published in ACM SIGGRAPH (1992-2003)
The complete post-training stack for LLMs, plus RL for any problem. SFT, DPO and multi-turn agentic RL, with evolutionary hyperparameter optimization built in.
sbi is a Python package for simulation-based inference, designed to meet the needs of both researchers and practitioners. Whether you need fine-grained control or an easy-to-use interface, sbi has you covered.
Examples for https://github.com/optuna/optuna
Real-time Web Dashboard for Optuna.
Perpetual is a high-performance gradient boosting machine. It delivers optimal accuracy in a single run without complex tuning through a simple budget parameter. It features out-of-the-box support for causal ML, continual learning, native calibration, and robust drift monitoring, along with Rust core and zero-copy bindings for Python and R
Automated modeling and machine learning framework FEDOT
Simulation and Parameter Estimation in Geophysics - A python package for simulation and gradient based parameter estimation in the context of geophysical applications.
The easiest way to read and WRITE from query parameters in sveltekit.
ComfyUI Lumi Batcher is a batch processing extension plugin designed for ComfyUI, aiming to improve workflow debugging efficiency. Traditional debugging methods require adjusting parameters one by one, while this tool significantly enhances work efficiency through batch processing capabilities.
Python package for Causal Discovery by learning the graphical structure of Bayesian networks. Structure Learning, Parameter Learning, Inferences, Sampling methods.